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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2025, Vol. 59 Issue (10): 2067-2077    DOI: 10.3785/j.issn.1008-973X.2025.10.007
    
Two-stage linguistic FMEA method for risk evaluation within complex product development process
Furong RUAN1(),Nanping FENG1,*(),Ting HUANG1,2,Shanlin YANG1,2
1. School of Management, Hefei University of Technology, Hefei 230009, China
2. Key Laboratory of Process Optimization and Intelligent Decision Making of Ministry of Education, Hefei 230009, China
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Abstract  

A linguistic failure mode and effects analysis (FMEA) method for risk evaluation based on personalized individual semantics was proposed, as the existing FMEA method has deficiencies in linguistic information expression, linguistic modeling, and factor assignment when assessing risk problems in complex situations. A group of evaluation experts used a distributed linguistic preference relation to evaluate failure patterns for each risk factor, while another group also used the distributed linguistic preference relation to evaluate the relative importance of evaluation experts and risk factors. The obtained linguistic preference relation was converted into a corresponding numerical preference relationship through the numerical scale model. A first-stage personalized individual semantic model was constructed to obtain the weight values of risk factors and evaluation experts, and a second-stage personalized individual semantic model was further constructed. Based on the solution results of the two-stage personalized individual semantic model, the final score of the failure mode was calculated, and the prioritization was completed. The risk assessment problems in the development process of a certain aero engine were selected for verification, and the results showed the feasibility and effectiveness of the proposed linguistic FMEA method. Experimental comparison with the uniformly distributed method shows that the personalized individual semantics obtained by the proposed method yield highly consistent risk-assessment outcomes, supporting reliability and accuracy in complex product development.

Key words： risk assessment      failure mode and effect analysis      distributed linguistic preference relation      personalized individual semantics      complex product development     
Received: 10 September 2024      Published: 27 October 2025
CLC:  C 931  
Fund:  国家社会科学基金资助项目（22BGL034）.
Corresponding Authors: Nanping FENG     E-mail: lotus087@163.com;fengnp@hfut.edu.cn
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Furong RUAN
Nanping FENG
Ting HUANG
Shanlin YANG
Cite this article:

Furong RUAN,Nanping FENG,Ting HUANG,Shanlin YANG. Two-stage linguistic FMEA method for risk evaluation within complex product development process. Journal of ZheJiang University (Engineering Science), 2025, 59(10): 2067-2077.

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https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.10.007     OR     https://www.zjujournals.com/eng/Y2025/V59/I10/2067


面向复杂产品研制过程风险评估的两阶段语言型FMEA方法

失效模式与影响分析（FMEA）方法在语言信息表达、语言建模、因子赋权上无法完全适应复杂情境下的风险评估活动，为此提出基于个性化语义的语言型FMEA风险评估方法. 一组专家采用分布式语言偏好关系对风险因子和评价专家的重要性进行评价，另一组专家采用分布式语言偏好关系对风险模式进行评价，利用数值刻度模型将语言评价结果转化为相应的数值偏好关系. 构建第一阶段的个性化语义模型，得到风险因素和评价专家的权重值，基于权重值构建第二阶段的个性化语义模型，算出失效模式的最终得分并完成优先级排序. 选取某型航空发动机研制过程中的风险评估问题，验证所提语言型FMEA方法的可行性与有效性. 实验结果表明，相比均匀分布方法，所提方法得到的个性化语义在获得高一致性水平风险评估结果方面具有优越性，有助于提升复杂实践背景中风险评估结果的可靠性与准确性.


关键词： 风险评估,  失效模式与影响分析,  分布式语言偏好关系,  个性化语义,  复杂产品开发 
Fig.1 Framework of linguistic FMEA method for risk evaluation based on personalized individual semantics
piL1L2L3
p1非常低非常轻微非常不可以
p2低轻微不可以
p3较低较轻微较不可以
p4中等中等中等
p5较高较高较可以
p6高高可以
p7非常高非常高非常可以
Tab.1 Linguistic evaluation terminology set for failure modes
piLeLr
p1非常不重要非常不重要
p2不重要不重要
p3中等较不重要
p4重要中等
p5非常重要较重要
p6—重要
p7—非常重要
Tab.2 Linguistic evaluation terminology sets for importance of experts and risk factors
RFiEX1EX2EX3EX4
RF10.81910.84540.90060.8622
RF20.85900.87730.80670.8307
RF30.84370.84400.85740.7917
加权一致性：0.8434
Tab.3 Optimal consistency on linguistic evaluation of risk types by domain experts
RFipiEX1EX2EX3EX4
RF1p1
p2
p3
p4
p5
p6
p7		0.0000
0.3631
0.3928
0.4999
0.6072
0.6370
0.9999		0.0000
0.3596
0.4573
0.5001
0.5428
0.6405
0.9999		0.0000
0.3725
0.4811
0.4999
0.5188
0.6275
0.9999		0.0000
0.3741
0.4684
0.5000
0.5317
0.6259
0.9999
RF2p1
p2
p3
p4
p5
p6
p7		0.0000
0.3277
0.4191
0.5000
0.5809
0.6724
0.9999		0.0000
0.3217
0.3735
0.5000
0.6265
0.6784
0.9999		0.0000
0.3675
0.4631
0.4999
0.5369
0.6326
0.9999		0.0000
0.3687
0.4644
0.5001
0.5357
0.6313
0.9999
RF3p1
p2
p3
p4
p5
p6
p7		0.0000
0.3736
0.4619
0.5001
0.5381
0.6264
0.9999		0.0000
0.2980
0.4483
0.4999
0.5517
0.7021
0.9999		0.0000
0.3738
0.4702
0.4999
0.5298
0.6262
0.9999		0.0000
0.2475
0.4073
0.5001
0.5928
0.7525
0.9999
Tab.4 Personalized semantic solutions under optimal consistency
RFiEX1EX2EX3EX4
RF10.76890.79780.76780.8022
RF20.81560.82000.76890.7722
RF30.75780.80220.79670.7622
加权一致性：0.7853
Tab.5 Consistency level on linguistic evaluation of risk types under uniform distribution
Fig.2 Risk type score in development process of certain aero engine
方法信息表达形式信息处理方法权重的确定方法
直接评价间接评价直接量化考虑语义主观赋权客观赋权综合赋权
Liu等[13]区间二型模糊集—√————
王睿等[15]直觉乘法偏好关系—√———专家评估+理想解模型法(风险因子)
Wang等[40]三角模糊数—√————
Huang等[41]—比例犹豫模糊语言集√—最好最坏法(风险因子)——
Zhang等[20]—分布式语言偏好信息—√———
Huang等[42]语言Z数—√——最小方差法(FMEA成员)；优劣解距离法(风险因子)—
赵翼翔等[43]直觉模糊数—√———层次分析法+数据本身(风险因子)
王济干等[44]语言分布评价—√——熵权法(风险因子)—
Salah等[45]绝对数值评价—√————
Song等[46]双向编码器表征法模型+向量空间模型—√——熵权法(风险因子)—
Liu等[47]模糊语言集—√——专家间关系+风险评估信息(FMEA成员)—
本研究—分布式语言偏好信息—√——专家评估+一致性最优个性化语义模型(FMEA成员+风险因子)
Tab.6 Comparison of linguistic FMEA methods for risk evaluation
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